Mechanical experiment teaching system for measuring motion parameters through multi-element combination
By integrating multi-sensor data and conducting virtual comparison experiments, the problem of low precision in single-measurement methods in existing mechanical experimental devices has been solved. Multi-dimensional synchronous measurement and error analysis have been achieved, enhancing the comprehensiveness and advanced nature of experimental teaching.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU NORMAL UNIVERSITY
- Filing Date
- 2026-01-14
- Publication Date
- 2026-04-14
AI Technical Summary
Existing mechanical experimental teaching devices have limited measurement parameters and accuracy, lack multi-source information fusion and cross-verification mechanisms, making it difficult for students to deeply understand measurement principles and error analysis. The theoretical model simulation is separated from the real experiment, making dynamic comparison impossible.
A reconfigurable experimental platform and multi-sensor measurement modules are adopted, combined with a central processing and display system, to achieve multi-sensor data fusion and real-time calculation. A virtual comparison experimental module and autonomous fault diagnosis function are introduced. Through cross-verification of redundant sensors, multi-dimensional synchronous measurement and comparison are achieved.
It enables high-precision generation and real-time quantitative comparison of multi-parameter curves, cultivates students' systematic thinking and engineering problem-solving abilities, and enhances the comprehensiveness and advanced nature of experimental teaching.
Smart Images
Figure CN121861982A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of mechanical experimental teaching technology, and in particular to a mechanical experimental teaching system for determining motion parameters by multiple combinations. Background Technology
[0002] In mechanics experimental teaching, traditional experimental devices with discrete and highly pre-defined configurations are commonly used, such as inclined troughs, air-cushioned rails with ticker timers or single photoelectric gates. These devices have limited measurement parameters, limited accuracy, and unintuitive experimental phenomena. Furthermore, the systems are closed and difficult to expand. While some digital experimental systems with integrated sensors have emerged with the development of teaching technology, these sensors are mostly fixed in configuration, the measurement methods are rigid, and the data streams are independent. They lack effective multi-source information fusion and cross-verification mechanisms. Students often passively receive data during experiments and find it difficult to deeply understand the differences between different measurement principles, the errors caused by numerical calculations, and the diagnostic logic when sensors fail. In addition, theoretical model simulations are usually separated from the actual experimental process, making it impossible to achieve dynamic and quantitative comparisons based on real initial conditions. Summary of the Invention
[0003] To address the aforementioned problems, this application provides a mechanical experimental teaching system for determining motion parameters using a multi-element combination method.
[0004] This application provides a mechanical experimental teaching system for determining motion parameters using a multi-element combination method, which adopts the following technical solution:
[0005] A mechanical experimental teaching system for determining motion parameters using multiple combinations includes:
[0006] A reconfigurable experimental platform, which is used to build various classical mechanical motion models;
[0007] A multi-sensor measurement module is integrated and installed on the reconfigurable experimental platform. The multi-sensor measurement module is used to synchronously or selectively measure the displacement, velocity, acceleration and force parameters of a moving object.
[0008] A central processing and display system is communicatively connected to the multi-sensor measurement module. The central processing and display system is used to receive and fuse data from various sensors, and to calculate and display multiple kinematic and dynamic parameter curves of the moving object in real time based on a unified spatiotemporal reference.
[0009] As a preferred technical solution of this application, the reconfigurable experimental platform includes a basic guide rail, an inclined plane module, a spring oscillator module, and a pulley force transmission module;
[0010] The multi-sensor measurement module includes a high frame rate visual sensor, a triaxial accelerometer, a laser displacement sensor, and a dynamic force sensor. The high frame rate visual sensor, triaxial accelerometer, laser displacement sensor, and dynamic force sensor are detachably installed at the corresponding measurement positions on the basic guide rail through a unified installation interface.
[0011] As a preferred technical solution of this application, the central processing and display system includes a data fusion unit. The data fusion unit is used to receive the image sequence captured by the high frame rate visual sensor, calculate the first displacement curve of the object through an image recognition algorithm, and receive the second displacement data measured by the laser displacement sensor. Based on the second displacement data, the first displacement curve is time-scaled and accuracy-compensated to generate high-precision standard displacement-time data.
[0012] As a preferred technical solution of this application, the central processing and display system further includes a parameter derivation unit. The parameter derivation unit is used to perform numerical differentiation on the standard displacement-time data to calculate and generate a velocity-time curve and a first acceleration-time curve. The parameter derivation unit is also used to receive the second acceleration-time curve measured by the triaxial accelerometer. By comparing and displaying the first acceleration-time curve and the second acceleration-time curve in the same coordinate system, the measurement error and the characteristics of the numerical differentiation algorithm can be revealed.
[0013] As a preferred technical solution of this application, the output end of the central processing and display system is communicatively connected to a virtual comparison experiment module. The virtual comparison experiment module is used to receive the actual experimental initial conditions measured by the multi-sensor measurement module, and based on the same initial conditions, call a preset ideal physical model to perform simulation calculations to generate theoretical motion parameter curves. The virtual comparison experiment module superimposes and compares the theoretical motion parameter curves with the measured motion parameter curves generated by the parameter derivation unit, and automatically calculates and labels the relative errors of key points.
[0014] As a preferred technical solution of this application, the virtual comparison experiment module is configured such that when the user replaces the replaceable module on the reconfigurable experimental platform, the virtual comparison experiment module can automatically identify the current experimental configuration and call the ideal physical model that matches it for simulation.
[0015] As a preferred technical solution of this application, the output end of the central processing and display system is communicatively connected to an autonomous fault setting and diagnosis module. The autonomous fault setting and diagnosis module is configured to: provide a human-machine interface for users to select preset fault types, including sensor bias, guide rail tilt angle error, and abnormal increase in friction; inject corresponding simulated fault signals into the data fusion unit or the parameter derivation unit according to the fault type selected by the user, so that the final displayed motion parameter curve produces a preset deviation; record and analyze the user's interactive comparison operation between the fault curve and the normal theoretical curve; and evaluate the user's diagnostic conclusion on the cause of the fault.
[0016] As a preferred technical solution of this application, when a user diagnoses a fault involving a specific sensor, the autonomous fault setting and diagnosis module can guide the user to enable or disable other redundant sensors in the multi-sensor measurement module for cross-verification, and the parameter derivation unit synchronously generates comparison curves based on different sensor combinations to assist in completing the diagnosis process.
[0017] In summary, this application includes the following beneficial technical effects:
[0018] This application utilizes high-precision data fusion from laser and vision sensors to generate a reliable motion benchmark, and derives multi-parameter curves based on this benchmark. This enables multi-dimensional synchronous measurement and comparison of the same physical process. The system not only allows for real-time, quantitative comparison between theory and experiment through virtual simulation, but also introduces optional sensor-level fault setting and diagnosis functions. It guides students to perform cross-validation by operating redundant sensors, gaining a deep understanding of measurement principles, error sources, and fault analysis logic. By integrating abstract physical concepts, measurement techniques, data processing, and error analysis, this application strengthens students' practical abilities while cultivating their systemic thinking and engineering problem-solving skills, thus enhancing the comprehensiveness and advanced nature of experimental teaching. Attached Figure Description
[0019] Figure 1 This is the architecture diagram of the mechanics experimental teaching system of this application. Detailed Implementation
[0020] The following is in conjunction with the appendix Figure 1 This application will be described in further detail.
[0021] See Figure 1 A mechanical experimental teaching system for determining motion parameters using multiple combinations, comprising:
[0022] The reconfigurable experimental platform is used to build various classical mechanical motion models. The reconfigurable experimental platform includes a basic guide rail, an inclined plane module, a spring oscillator module, and a pulley force transmission module.
[0023] The multi-sensor measurement module is integrated and installed on the reconfigurable experimental platform. It is used to synchronously or selectively measure the displacement, velocity, acceleration, and force parameters of a moving object. The multi-sensor measurement module includes a high-frame-rate visual sensor, a triaxial accelerometer, a laser displacement sensor, and a dynamic force sensor. These sensors are detachably mounted on the corresponding measurement positions of the base rail through a unified mounting interface.
[0024] The central processing and display system (CPU) is communicatively connected to the multi-sensor measurement module. The CPU receives and fuses data from various sensors, and calculates and displays multiple kinematic and dynamic parameter curves of the moving object in real time based on a unified spatiotemporal reference. The CPU includes a data fusion unit, which receives image sequences captured by a high-frame-rate visual sensor, calculates the object's first displacement curve using image recognition algorithms, and also receives second displacement data measured by a laser displacement sensor. Using the second displacement data as a reference, the first displacement curve is time-scaled and accuracy-compensated to generate high-precision standard displacement-time data.
[0025] First, users select and assemble the required physical modules (such as inclined planes, pulleys, spring oscillators, etc.) on the basic guide rail according to the teaching or experimental objectives, constructing target mechanical models such as uniformly accelerated linear motion, simple harmonic motion, and connected body motion. Then, based on the parameters to be observed, users select one or more from high frame rate vision sensors, laser displacement sensors, triaxial accelerometers, and dynamic force sensors, and conveniently fix them to the preset slot positions next to the guide rail or on the moving object through standardized installation interfaces. Then, the experiment is started, the object begins to move, and all deployed sensors start working under the synchronous clock command of the central processing system. The high frame rate vision sensor captures a continuous image sequence, and the embedded image processing unit identifies and tracks the object's marker points in real time, calculating the displacement sequence based on the image coordinate system (first displacement data). The laser displacement sensor, with a higher sampling rate and absolute measurement principle, directly outputs a high-precision displacement-time data (second displacement data).
[0026] The core operation of the data fusion unit of the central processing system is to use laser displacement sensor data as a high-precision spatiotemporal "ruler" and perform timestamp calibration, system error compensation and scale normalization on the first displacement data calculated by the visual sensor through relevant algorithms, and finally synthesize a "standard displacement-time data" that has both high sampling rate (visual advantage) and high absolute accuracy (laser advantage).
[0027] The data fusion unit uses the data from the laser displacement sensor as a reference to fuse the displacement data from the high frame rate vision sensor. The specific process is as follows: First, time-stamp alignment is performed. Since the two sensors are independent devices, their internal clocks have slight deviations. The system uses cross-correlation analysis to calculate the phase difference between the two original displacement data sequences. Using the high-precision time-stamp of the laser sensor as a reference, the data sequence from the vision sensor is time-axis shifted and resampled to ensure strict synchronization of the timestamps of each data point. Second, accuracy compensation is performed. Through a synchronous acquisition static calibration stage, the system obtains the time difference of the same stationary object within the same measurement interval. The absolute position value measured by the sensor (as the true value) and the pixel coordinate value calculated by the vision sensor are used to fit a coordinate transformation model (usually a linear or second-order polynomial model) using the least squares method. In dynamic measurement, this model is applied in real time to the "first displacement data" calculated by the vision sensor, transforming it from image pixel coordinates to the physical world coordinate system consistent with that of the laser sensor. The high-precision sampling points of the laser sensor are used as control points to perform piecewise linear interpolation or smooth spline compensation on the transformed visual data sequence, thereby synthesizing a "standard displacement-time data" that combines high-frequency sampling of vision and high absolute precision of laser.
[0028] The central processing and display system also includes a parameter derivation unit, which is used to perform numerical differentiation on standard displacement-time data to calculate and generate velocity-time curves and a first acceleration-time curve. The parameter derivation unit is also used to receive the second acceleration-time curve measured by the triaxial accelerometer. By comparing and displaying the first acceleration-time curve and the second acceleration-time curve in the same coordinate system, the measurement error and the characteristics of the numerical differentiation algorithm can be revealed.
[0029] The data fusion unit sends standard displacement data to the parameter derivation unit, which processes it using an optimized numerical differentiation algorithm (such as the five-point interpolation differentiation method) to calculate the velocity-time curve and the "first acceleration-time curve" (which is the indirect acceleration calculated from displacement differentiation). In another parallel channel, the parameter derivation unit directly receives the "second acceleration-time curve" (which is the directly measured acceleration) from the triaxial accelerometer attached to the moving object. Subsequently, the system overlays these two acceleration curves, which are derived from different measurement principles, on the same coordinate interface. This direct graphical comparison can vividly reveal the calculation characteristics such as phase lag and noise amplification brought about by the numerical differentiation process, as well as the measurement characteristics such as sensor dynamic response and installation deviation, realizing mutual verification and principle teaching between different measurement methods.
[0030] The parameter-derived unit performs numerical differentiation on the "standard displacement-time data" to calculate velocity and acceleration. The "optimized numerical differentiation algorithm" used is specifically a five-point central difference method combined with smoothing preprocessing. The specific steps are as follows: For the standard displacement sequence {s(t_i)}, firstly, a Savitzky-Golay filter (window size 5, polynomial order 2) is used for smoothing to suppress high-frequency noise. Then, for the smoothed sequence, at the midpoint t_i, using five data points (two points before and two points after it, for a total of five points), the formula v is used. (t_i)≈[s(t_{i-2})-8*s(t_{i-1})+8*s(t_{i+1})-s(t_{i+2})] / (12*Δt) to calculate the instantaneous velocity v(t_i); apply the same smoothing and five-point center difference formula to the obtained velocity sequence {v(t_i)} to calculate the "first acceleration-time curve" a_c(t_i). Compared with simple difference, this method can effectively reduce random errors and has higher phase preservation characteristics, which is beneficial for comparison with measured accelerometer data.
[0031] The output of the central processing and display system is connected to a virtual comparison experiment module. This module receives the actual initial experimental conditions measured by the multi-sensor measurement module and, based on the same initial conditions, calls a preset ideal physical model to perform simulation calculations, generating theoretical motion parameter curves. The virtual comparison experiment module overlays and compares these theoretical motion parameter curves with the measured motion parameter curves generated by the parameter derivation unit, and automatically calculates and labels the relative errors of key points. The virtual comparison experiment module is configured so that when the user replaces a replaceable module on the reconfigurable experimental platform, it can automatically identify the current experimental configuration and call a matching ideal physical model for simulation.
[0032] While the experiment is underway, the virtual comparison experiment module starts working, automatically identifying the current experimental model in two ways:
[0033] Receive manual selections from users on the system interface;
[0034] Intelligent judgment is made by analyzing the feedback data of each sensor at the initial moment (such as the initial value of the dynamic force sensor and the reading of the guide rail tilt sensor).
[0035] Once the model is determined (e.g., "a spring oscillator with viscous damping"), the module immediately calls the embedded dynamic equations of the corresponding ideal physical model and uses the actual initial conditions (such as initial displacement, initial velocity, and initial force) measured by the multi-sensor measurement module at the beginning of the experiment as the initial values to perform high-precision numerical simulation, generating a complete set of theoretical displacement, velocity, and acceleration curves. This set of theoretical curves is superimposed on the measured curves in real time. The system automatically identifies key feature points such as motion period, extreme points, and equilibrium positions, and calculates and marks the percentage of relative error between the measured values and theoretical values at these points, realizing an instantaneous, quantitative, and visual comparison between theoretical predictions and experimental results.
[0036] The automatic identification logic of the virtual comparison experiment module is as follows: Before the experiment begins or at the initial moment, the system reads the "static" or "initial state" data of each sensor to form a feature vector. For example, by reading the initial tension value F0 of the dynamic force sensor when the object is stationary, it determines whether there is pulley traction; by reading the static reading of the triaxial accelerometer before the object is released, it calculates the static tilt angle of the guide rail; by analyzing the linear relationship between the initial displacement and the dynamic force sensor reading when the spring oscillator module is connected, it determines the Hooke coefficient. The system matches these features with a preset "experimental configuration feature database". This database defines rules such as "with tilt angle, with traction force, no elastic force" corresponding to "inclined plane traction model"; "tilt angle close to zero, with periodic elastic force, no external traction" corresponding to "horizontal spring oscillator model", etc. When the user manually selects, the manual selection takes precedence; when no manual selection is made, the system executes the above automatic matching process and uses the configuration with the highest matching degree and initial parameters (such as the automatically calculated tilt angle and spring stiffness coefficient) as the basis for calling the corresponding ideal physical model simulation.
[0037] The output of the central processing and display system is connected to an autonomous fault setting and diagnosis module. This module is configured to provide a human-machine interface for users to select preset fault types, including sensor bias, guide rail tilt error, and abnormal increase in friction. Based on the user-selected fault type, it injects corresponding simulated fault signals into the data fusion unit or parameter derivation unit, causing a preset deviation in the final displayed motion parameter curve. It records and analyzes the user's interactive comparison between the fault curve and the normal theoretical curve, evaluating their diagnostic conclusions regarding the cause of the fault. When the user's fault diagnosis involves a specific sensor, the autonomous fault setting and diagnosis module can guide the user to enable or disable other redundant sensors in the multi-sensor measurement module for cross-validation. Simultaneously, the parameter derivation unit generates comparison curves based on different sensor combinations to assist in the diagnostic process.
[0038] Users can select one or more preset faults from the fault library through the human-machine interface of the autonomous fault setting and diagnosis module (e.g., "add a constant bias of +0.5g to the accelerometer Z-axis data", "increase the actual tilt angle setting value of the guide rail by 2 degrees", or "introduce an additional constant sliding friction force in the motion equation simulation"). The autonomous fault setting and diagnosis module then injects the mathematical model of the fault into the corresponding link of the data processing pipeline in the background: the bias fault will directly modify the original sensor data stream, and the model parameter fault will affect the theoretical calculation of the virtual comparison experiment module or the calibration parameters in data fusion. After the fault is injected, the "measured curve" or "theoretical comparison curve" observed by the user at the front end will produce abnormal deviations that conform to physical laws. The system will propose a diagnostic task to the user, requiring them to determine the possible causes of the fault by analyzing the abnormal shape of the curve.
[0039] When a user suspects a fault may originate from a specific sensor (e.g., a suspected accelerometer malfunction), they can, under system guidance, disable the data stream of that suspected sensor through software switching or physical shutdown. The central processing system then uses data from other redundant sensors (such as laser and vision sensors) to generate another set of "verification curves" through data fusion and numerical differentiation. For example, after disabling the accelerometer, the system obtains the acceleration curve entirely from the high-precision fused displacement data differentiation. If this curve significantly improves the agreement with the virtual theoretical curve, while the original accelerometer curve shows a deviation, it strongly confirms the hypothesis of an accelerometer malfunction. By operating different sensor combinations and observing the changes in the comparative curves, the user can ultimately pinpoint the source of the fault and submit a diagnostic conclusion. The system then provides evaluation feedback. This process fully simulates the complete logical closed loop of system status monitoring and fault diagnosis based on multi-sensor information fusion in engineering practice.
[0040] The autonomous fault setting and diagnosis module maintains a fault library containing fault types, affected objects, and mathematical models. For example, for the fault "accelerometer Z-axis offset +0.5g", its model is a_corrupted(t) = a_raw(t) + 0.5*g, where a_raw(t) is the raw accelerometer data and g is the gravitational acceleration. This module replaces the original a_raw(t) data stream with the corrected a_corrupted(t) data stream between the data acquisition layer and the data processing layer through redirection or middleware interception, and passes it to the downstream parameter derivation unit. For the fault "guide rail tilt angle error increased by 2 degrees", it affects both theoretical simulation and data fusion: in the virtual comparison experiment module, it modifies the tilt angle parameter θ in the simulation model to θ + 2°; in the data fusion unit, if the calibration algorithm involves gravity component correction, it also uses the incorrect tilt angle value. Fault injection is real-time and dynamic. Users can "enable" or "disable" specific faults by selecting fault types during experiments or playback analysis to observe the causal relationship of curve changes.
[0041] The activation and deactivation control process for redundant sensor cross-validation is as follows: At the software level, the system maintains a programmable data channel switch matrix for each sensor (such as "accelerometer A", "laser sensor L", and "vision sensor V"). When the user clicks "deactivate accelerometer A" on the diagnostic interface, the autonomous fault setting and diagnosis module sends a command to the data acquisition scheduler of the central processing system. The scheduler will ignore all new data from the accelerometer hardware port or mark it as "invalid". The parameter derivation unit will receive a sensor status change notification, and its internal algorithm will automatically switch to the backup calculation path. For example, when calculating acceleration, it will switch from the default "directly read accelerometer data" path to the path "fully rely on numerical differentiation to calculate acceleration from displacement data fused by laser and vision". At the physical level, the system can physically de-energize the sensor by sending a relay control signal to the sensor power supply circuit, but it usually uses a software deactivation method. The system will synchronously record every sensor activation / deactivation operation performed by the user and refresh and display all curves calculated based on the current effective sensor combination in real time for user comparison and analysis.
[0042] The specific calculations for theoretical model simulation and automatic error annotation are as follows: The theoretical model embedded in the system is described in the form of a system of ordinary differential equations. For example, for a "spring oscillator with viscous damping," the equation is m*a=-k*xc*v. The system uses the fourth-order Runge-Kutta method to numerically integrate and solve this differential equation. The initial displacement x0 and velocity v0 are provided by the actual measured values of the multi-element sensor measurement module at the instant of experimental triggering, generating theoretical curves. For "automatic identification of key points," the system uses a peak detection algorithm (comparing the values of adjacent sampling points) to find all local maxima and minima on the measured displacement / velocity / acceleration curves. The system identifies "extreme points"; points with zero velocity are identified using a zero-crossing detection algorithm and designated as "highest / lowest points"; the motion period is determined through periodic analysis (such as the autocorrelation function method); at each identified key point timestamp t_key, the system reads the theoretical value Y_theory(t_key) and the measured value Y_measured(t_key), and then calculates the relative error: Error (%) = |Y_theory - Y_measured| / |Y_theory|*100%; the calculation results are dynamically labeled in the form of text tags near the curve of the corresponding point to achieve quantitative comparison.
[0043] This application utilizes high-precision data fusion from laser and vision sensors to generate a reliable motion benchmark, and derives multi-parameter curves based on this benchmark. This enables multi-dimensional synchronous measurement and comparison of the same physical process. The system not only allows for real-time, quantitative comparison between theory and experiment through virtual simulation, but also introduces optional sensor-level fault setting and diagnosis functions. It guides students to perform cross-validation by operating redundant sensors, gaining a deep understanding of measurement principles, error sources, and fault analysis logic. By integrating abstract physical concepts, measurement techniques, data processing, and error analysis, this application strengthens students' practical abilities while cultivating their systemic thinking and engineering problem-solving skills, thus enhancing the comprehensiveness and advanced nature of experimental teaching.
[0044] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. A mechanical experimental teaching system for determining motion parameters using a multi-element combination method, characterized in that, include: A reconfigurable experimental platform, which is used to build various classical mechanical motion models; A multi-sensor measurement module is integrated and installed on the reconfigurable experimental platform. The multi-sensor measurement module is used to synchronously or selectively measure the displacement, velocity, acceleration and force parameters of a moving object. A central processing and display system is communicatively connected to the multi-sensor measurement module. The central processing and display system is used to receive and fuse data from various sensors, and to calculate and display multiple kinematic and dynamic parameter curves of the moving object in real time based on a unified spatiotemporal reference.
2. The mechanical experimental teaching system for determining motion parameters by multiple combinations according to claim 1, characterized in that, The reconfigurable experimental platform includes a basic guide rail, an inclined plane module, a spring oscillator module, and a pulley force transmission module; The multi-sensor measurement module includes a high frame rate visual sensor, a triaxial accelerometer, a laser displacement sensor, and a dynamic force sensor. The high frame rate visual sensor, triaxial accelerometer, laser displacement sensor, and dynamic force sensor are detachably installed at the corresponding measurement positions on the basic guide rail through a unified installation interface.
3. The mechanical experimental teaching system for determining motion parameters by multiple combinations according to claim 2, characterized in that, The central processing and display system includes a data fusion unit, which is used to receive the image sequence captured by the high frame rate visual sensor, calculate the first displacement curve of the object through an image recognition algorithm, and receive the second displacement data measured by the laser displacement sensor. Using the second displacement data as a reference, the first displacement curve is time-scaled and accuracy-compensated to generate high-precision standard displacement-time data.
4. The mechanical experimental teaching system for determining motion parameters by multiple combinations according to claim 3, characterized in that, The central processing and display system also includes a parameter derivation unit, which is used to perform numerical differentiation on the standard displacement-time data to calculate and generate a velocity-time curve and a first acceleration-time curve. The parameter derivation unit is also used to receive the second acceleration-time curve measured by the triaxial accelerometer and to reveal the measurement error and the characteristics of the numerical differentiation algorithm by comparing and displaying the first acceleration-time curve and the second acceleration-time curve in the same coordinate system.
5. The mechanical experimental teaching system for determining motion parameters by multiple combinations according to claim 4, characterized in that, The output of the central processing and display system is connected to a virtual comparison experiment module. The virtual comparison experiment module is used to receive the actual experimental initial conditions measured by the multi-sensor measurement module, and based on the same initial conditions, call the preset ideal physical model to perform simulation calculations to generate theoretical motion parameter curves. The virtual comparison experiment module overlays and compares the theoretical motion parameter curves with the measured motion parameter curves generated by the parameter derivation unit, and automatically calculates and labels the relative errors of key points.
6. The mechanical experimental teaching system for determining motion parameters by multiple combinations according to claim 5, characterized in that, The virtual comparison experiment module is configured such that when the user replaces the replaceable module on the reconfigurable experimental platform, the virtual comparison experiment module can automatically identify the current experimental configuration and call the matching ideal physical model for simulation.
7. A mechanical experimental teaching system for determining motion parameters using a multi-element combination method according to claim 6, characterized in that, The output of the central processing and display system is communicatively connected to an autonomous fault setting and diagnosis module. The autonomous fault setting and diagnosis module is configured to: provide a human-machine interface for users to select preset fault types, including sensor bias, guide rail tilt angle error, and abnormal increase in friction; inject corresponding simulated fault signals into the data fusion unit or the parameter derivation unit according to the fault type selected by the user, so that the final displayed motion parameter curve produces a preset deviation; record and analyze the user's interactive comparison operation between the fault curve and the normal theoretical curve; and evaluate the user's diagnostic conclusion on the cause of the fault.
8. A mechanical experimental teaching system for determining motion parameters using a multi-element combination method according to claim 7, characterized in that, When a user diagnoses a fault involving a specific sensor, the autonomous fault setting and diagnosis module can guide the user to enable or disable other redundant sensors in the multi-sensor measurement module for cross-validation, and the parameter derivation unit can synchronously generate comparison curves based on different sensor combinations to assist in completing the diagnosis process.